Genome-wide association studies in pharmacogenomics .
Pharmacogenomics is defined as the study of genes and how an individual response is affected due to drugs. Pharmacogenomics is an emerging new branch with combination of both pharmacology (the branch of science that deals with study of drugs) as well as genomics (the branch of science that deals with study of genes) for development of effective doses and safe medications tailored according an individual patient genetic makeup. Human Genome Project is one of the crucial projects in which researchers are developing and learning relation in genes and its effect on the body’s response to medications. Difference in genetic makeup provides difference in effectiveness of medication and in future to predict effectiveness of medication for an individual and to study existence of adverse drug reactions. Besides advancement in the field of science and technology till date pharmacogenomics hangs in infancy. There is limited use of pharmacogenomics, but still, novel approaches are under clinical trials. In near future, pharmacogenomics will enable development of tailor-made therapeutics for treating widespread health problems like neurodegenerative, cardiovascular disorders, HIV, cancer, asthma, etc.
- personalized medicines
- tailored drugs
1. Pharmacogenomics: overview
Due to variability existence among individuals against drug therapy response, it is a challenging task to predict the degree of effectiveness of a medication to a particular patient. As we know various clinical factors which are known to affect drug response, for example body size, age, sex, hepatic and renal function, and associated drug use (Table 1). Along with these clinical factors, some pharmacological factors also play a major role which includes differences in metabolism, drug distribution and drug directed proteins [2, 3]. Recently, major causes of interindividual differences are shown by variations in genes encoding cytochrome P450 (CYP) and other metabolizing enzymes in plasma concentrations of some drugs [4, 5].
|Drug-metabolizing enzymes||Drug metabolism (pharmacokinetics)|
|Therapeutic targets||Drug efficacy (pharmacodynamics)|
|Targets of ADRs||Drug toxicity|
|Drug transporters||Drug disposition|
|Environmental chemicals, alcohol drinking, combined drugs effect, and dietary substances||Drug efficacy, toxicity, and pharmacokinetics|
|Age, sex, pregnancy, exercise, disease state, starvation||Drug efficacy, toxicity, and pharmacokinetics|
Pharmacogenetics and pharmacogenomics can be used interchangeably. Though, Pharmacogenomics refers to the whole range of genes that are related to the determination of drug efficacy and safety whereas pharmacogenetics means monogenetic variants which alter the drug response [6, 7]. Pharmacogenomics is defined as study of genes and how they affect an individual response to the administered drugs. Pharmacogenomics is emerging new branch with combination of both pharmacology (branch of science which deals with study of drugs) as well as genomics (the branch of science which deals with study of genes) for development of effective doses and safe medications tailored according an individual patient genetic makeup (Figure 1) [8, 9].
Basically, the concept for pharmacogenetics was left unknown for more than 50 years. This study underlined to the molecular mechanisms in account for their variation in responses to drug due to inherited characters and in drug development process. Pharmacogenomics applications can be employed in the improvement of discovery of new entities and its development with two possible ways: target the new drug targets or development of new entity to overcome drug resistance, and another way is to optimize the pharmacokinetics and metabolism of drug for reduction of the drug level variations . In fact, personalized drug therapy or individualized drug therapy is not an easy task. It needs many folds as there may be a lack of information regarding drug action, genomic elements of important disease pathogenesis, especially for complex diseases. Also, large scale clinical studies are sometimes becoming a big challenge for the researchers . The correlation of pharmacogenomics and cancer would expand the specific anticancer drugs with better chemotherapeutic outcomes [12, 13, 14, 15]. There are prominent examples with recent clinical and pharmaceutical restrains where the molecular based mechanisms are involved in various drug responses were observed among the patients and diagnosed with the similar diseases [16, 17]. Moreover, various polymorphisms existence at genetic levels in genes found to have association with alteration in responses of drug and rate of ADRs in humans (Table 2) .
|TPMT||Anticancer drugs like 6-thiopurine, 6-Mercaptopurine, azathioprine||Toxicity|
|Human leukocyte antigens (HLA)||Abacavir||Related to hypersensitivity|
|N-acetyltransferase (NATs)||Sulphonamides, hydralazine, Isoniazid||Toxicity, hypersensitivity|
|UDP-glucuronosyltransferase 1A1 (UGT1A1)||Irinotecan||Toxicity|
|MDRI||Antiepileptic drugs||Drug response|
Finally, pharmacogenomics-based development of drug and its regulation will open the doors for new as well as targeted drug development for promoting safe, effective, and cost-effective drug therapy for individual. The theoretical origin for pharmacogenomics is outlined by Sir Archibald Garrod’s in book entitled as “1939 Inborn Factors of Disease” .
Pharmacogenetics is the study of how an individual person’s genes respond to a drug. This branch is associated with genomics is genetic level studies with functional studies and pharmacology (includes pharmacokinetics and pharmacodynamics). All these branches together aid in the development of safe, and effective medications along with doses which are probably tailored to an individual persona genetic makeup [20, 21, 22, 23, 24]. Pharmacogenetics is indicated as major clinically proven application in terms of advancement in human genomic science. This potentiates a revolution in drug therapy. As a result, diseases which range from depression to viral infection and from childhood leukemia to hypertension are treated or controlled for enhancing the quality of life of patient. Most medicines at present are available as “one medication fits all” but they sometimes were not capable to work same to everyone. So, it is difficult to envisage who will have benefit result and who will have negative side effects. Also, the knowledge which scientist have acquired due to extensive work on Human Genome Project and are learning about inherited variations of genes and there effect on body’s response to medications. Conditions in which responses of an individual to certain drugs include Stevens-Johnson syndrome or epidermal toxic necrolysis, clopidogrel resistance, malignant hyperthermia, warfarin sensitivity and its resistance and thiopurine S-methyltransferase deficiency .
Many common diseases having high morbidity as well as mortality rates have now known with well-established genetic components. The degree of role of genetics has been predicted for diseases like obesity and diabetes according to their sibling analysis [26, 27]. In the same way, some rare gene mutations can provide a vision into the more complex biological processes . For instance, when the subject possesses extreme levels of HDL in their blood, one can easily demonstrated the influence of CETP (cholesteryl ester transfer protein) on patients HDL levels [29, 30, 31]. In another case, a person having deactivating mutations due to the Janus kinase 3 (JAK 3) gene shows severe combination of immune-deficient syndrome, as sometimes inhibition of JAK3 was expected to affect the human immune suppression [32, 33]. Hence, this led to a new investigation on drugs having CETP inhibition and JAK3 inhibition with the help of pharmacogenetics . Also, with the advent of pharmacogenomics, the path of relationships between disease state and human genes has now established which led to the suitable selection of therapeutic targets.
Nowadays, many academic institutions and Pharmaceutical companies are moving toward the investigation on the relationship between disease phenotypes and genetic variations to better categorize diseases [35, 36]. Although the collection of medical phenotypes having linkages with samples of DNA provides a prominent opportunity for examine the genetic variation which are present in patients. Investigation of genetic variation can be done by collection of DNA of particular patient. This is characterized in a study where DNA from a person involves in trails of lipid lowering demonstrated a swift connection between phenotypic novel lipase gene family and for HDL levels. As per literature reports, above mentioned studies are based on a sound hypothesis which is linked to candidate’s biological gene selection. Now it is easy to cross-examine the genome selection which is solely depends on phenotypic criteria [10, 37]. These stages have now substituted around 300,000 SNPs across the genome, by exploiting only few haplotype-defining SNPs. Perlegen sciences have developed newly genotyping technologies which has with a capability of genotyping mass hundreds or thousands of markers with the help of high-density based oligonucleotide arrays linked with restriction enzyme-based genomic reduction. However, as these technologies advances, still exact number of haplotype-defining SNPs is uncertain. Some findings are recently reported relation to assess polymorphisms across selected gene regions recommends that, it is necessary to reach an r2 of >0.8% in order to detect more than 80% of all haplotypes. Due to HapMap project progression with defined LD patterns linkage, scientist working on genes will thorough assess to the degree of LD in a represented regions or selected regions. This will enable to explore more around selection of SNP regardless design of study [38, 39]. As genome approach does not depend upon selection of candidate genes, so understanding on complex diseases such as psychiatric or cardiovascular diseases will become more efficient. Some researchers believed that the new horizons on LD coverage about insights of human genome and SNP density will show the perception of a substantial genomic portion areas and its relation with interest of phenotype [40, 41]. To assess the Perlegen Sciences chip-based array-based platform and to justify the haplotype tagging approach for the identification of genetic associations, 7283 SNPs connecting 17.1 mega bases (Mb) of DNA were genotyped for detecting linkages with HDL levels. Further, SNPs were connected with 50 CETP haploblock gene were found out as the most valuable association in dataset. The companies like Perlegen and project like Hap Map project recently declared their purpose to provide it SNPs markers into public provinces to further advent to basis for such kind of experiments which help in the scientific community [42, 43].
Pharmacogenetics significantly expands the therapy outcomes and drug uses. Medications may prescribe in low dose under strict monitoring to patients which shows genetically predisposed to their adverse events. This would probably helpful for drugs having narrow therapeutic index such as warfarin may be started gradually in patients having VKORC1 genotype linked with improved warfarin sensitivity. With the help of pharmacogenetics, it is now possible to reduce the number of subjects to conduct any experiment and chances of error may be eliminated for many diseases [44, 45].
On the contrary, clinicians may be able to minimize possible adverse effects with the aid of genetic information for matching suitable drug to suitable patient at an appropriate dose. For instances, traditional approach to the management of hypertension involves the trial of numerous anti-hypertensive drugs till the desired blood pressure achieved with adequate drug tolerability. In this case, few initial drugs/agents fail to produce lower blood pressure or shown intolerable adverse effects. This way of selection of drugs took long time which ultimately suffered by patients. On the contrary, Pharmacogenetics, based on the patients’ DNA, offers the greatest response with the best tolerability of the drug. Based on genetic regulator of cellular functions, pharmacogenetics may be able to produce new drugs with less adverse effects. For example, chromosome translocation and its derived enzymes are responsible for causing life-threatening chronic myeloid leukemia (CML) which led to accelerate FDA approval of inhibitor of translocation-created enzyme Imatinib . In the end, this core subject improves the quality and cut-down the total costs of healthcare by minimizing the number of adverse reaction and reduce treatment failures gives rise to the discovery of new genetic targets for disease management [47, 48, 49].
3. Case studies
3.1 Thiopurine therapy and TPMT (thiopurine methyltransferase) testing
Thiopurine are the categories of drugs that are used to conquer the normal activity of the body’s immune system. In short, these are called antimetabolites chiefly used as an antiproliferative as well as immunosuppressants such as mercaptopurine and azathioprine. 6-mercaptopurine daily administered for 3–4 years for treating childhood leukemia, while azathioprine which is a prodrug of 6-mercaptopurine prescribed for treating inflammatory bowel disease (also known as Crohn’s disease) . TPMT methylates thiopurine compounds. S-adenosyl-L-methionine acts as methyl donor and converts it into S-adenosyl-L-homocysteine [51, 52]. So, TPMT metabolizes various thiopurine based drugs with mechanism of S-adenosyl-L-methionine while S-methyl acting as donor, while S-adenosyl-L-homocysteine acting as a derivative. Genetic polymorphism which affects basically enzymatic activity has association with variations in toxicity and sensitivity within individuals due to such drugs. Nearby 1/300 individual is lacking for this enzyme. TPMT has not recognized to have any phenotype in the absence of encounter drug. TPMT is now enlists by FDA as a pharmacogenomic biomarker for various adverse drug reactions related to cisplatin such as cisplatin-induced ototoxicity in teenagers [47, 53, 54, 55].
Patients having identical alleles at equivalent chromosomal loci accumulate unnecessary thioguanine nucleotides levels (up to 10-fold higher related with wild types) and treatment with standard dosages of drug and leading to a hematopoietic toxicity (pancytopenia and myelosuppression) which is life-threatening condition [56, 57].
In more concise way, patients having heterozygous gene variations are also at high risk in terms of toxicity and dosage reductions is prior in these cases up to their tolerate therapy. According to pharmacoeconomic studies, the determination of the TPMT genotype is cost-effective and it must be checked prior to the start of therapy. According to a review of the literature, it was found that TPMT testing with clinical performances for myelosuppression was estimated with specificity of 89%, sensitivity of 32%, 9% PPV and NPV of 97% (Table 3). The low estimated value represents low incidence of severity in myelosuppression especially in those patients who are carrier of not less than one defective allele. Researchers have estimated the net cost for avoidance of serious events of myelosuppression. Out of 1000 patients receiving azathioprine, only 3.2% (equivalent to 32 cases) have founded with severe leukopenia and TPMT screening avoided as third of those trials [50, 58].
|Positive predictive value (PPV)||60.0|
|Negative predictive value (PPV)||80.0|
Iorio and co-workers have analyzed drug responses on various human cancer cell lines. The mapping was done for around 11K tumors obtained from different 29 different human tissues as per Cancer of Genome Atlas (TCGA) enlisted from 1000 cancer cell lines as per Genomics of Drug Sensitivity in Cancer resource. In another event, TCGA patient gene expression was studied for drug response. In this, more than 140 gene drug interactions were studied with specific somatic biomarkers [59, 60, 61].
3.2 Abacavir therapy and HLA testing
Abacavir which is HIV-1 nucleoside with reverse transcriptase inhibition is employed for management of HIV/AIDS. It is well tolerated but sometimes shows common to more severe side-effects which include lactic acidosis, hypersensitivity . In some studies, it was observed that a genetic testing/marker can help in predicting whether a HIV-infected patient is at high risk of abacavir induced severe hypersensitivity reactions (approx. 5% of patients) [63, 64]. This hypersensitivity reaction accompanies with lethal gastrointestinal symptoms, rashes, and fever. This reaction is life threatening, particularly if drug is restarted and discontinued. One study has shown about an occurrence of human leukocyte antigen (HLA)B05701 is main cause of hypersensitivity [45, 65]. Based on the Australian cohort, patients were 114 times more hypersensitivity due to HLA-B5701 allele reaction, whereas in an industry-sponsored study revealed that patients with the HLA-B5701 allele was associated with 24 times more likely to experience of hypersensitivity reaction . Thus, one way to solve this issue is genetic testing which integrates pharmacogenetics into the clinical practice. The distribution of HLA-B5701 allele can be detected in many worldwide populations (Table 4).
|Population group||HLA-B5701 carrier frequency range (%)|
3.3 Statin therapy and polymorphic angiotensin-converting enzyme
Statins (HMGCoA) reductase inhibitors most often used in management of hypercholesterolemia condition accompanying with elevation in risk to coronary heart disease [66, 67]. Due to increased number of cases of hypercholesterolemia along with volume of statins related prescriptions in US, it creates a significant interest in optimization of costs related to these therapies . Recent investigations have told that polymorphism in I/D angiotensin converting enzyme (ACE) has correlation with risk of heart related syndromes in men when treated with statins [68, 69, 70]. Next 2 years of statin medication, in which males who are having DD genotype (equivalent to 27% of patients) shown to have no effect on the risk of coronary heart disease (with relative risk factor of 1.34), in comparison to males with ID (equivalent to 21% of patients) present a marked decreased in risk of coronary cardiovascular disease (with relative risk rate of 0.87), II genotype (equivalent to 22% of patients) having relative risk of 0.23, thus concluding that patients bearing DD genotype did not take advantages from statin treatment. Also, testing of I/D genotype might results in cost effective as few patients presents the I/I or I/D genotype .
3.4 Muscle relaxant succinylcholine and antitubercular drug, INH
These two conventional illustrations of pharmacogenetics involve the genetic variation along with enzymatic metabolism (enzymatic hydrolysis and acetylation). Both act as a monogenic trait and involved PK variations because of inheritance differences [71, 72]. It was observed that some patients with succinylcholine treatment experienced a serious and lethal adverse event i.e. prolonged muscle paralysis which is due to inherited “atypical” butyryl cholinesterase enzyme (BCHE). Later, it was established that BCHE allele which encodes the most usual atypical form of enzyme comprised with a nonsynonymous coding i.e. single nucleotide polymorphism (nSNP), G209 > A, results in Asp70 > Gly change in encoded amino acid which altered active sites of enzyme [73, 74]. But atypical BCHE has less ability to catalyze the succinylcholine hydrolysis and could resist to inhibition due to dibucaine compound [37, 75].
Tuberculosis is the most problematic disease of both developing as well as under-developed nations. The conformity to patients with tuberculosis is due to common lethal adverse reactions and supposed to have important aspect providing high prevalence [76, 77]. Many investigations showed that the polymorphisms of N-acetyl transferase 2 (NAT-2), CYP2E1 as well as glutathione S transferase (GST-1) would be able to influence concentration of liver toxic isoniazid metabolites in plasma. Some polymorphic genes contribute in the INH induced hepatotoxicity by altering the anti-oxidant enzyme expression, these gene polymorphisms include glucuronosyltransferase (UGT), basic region of leucine zipper factor family (CNC) homolo (BACH), human leukocyte antigen (HLA), nitric oxide synthase (NOS) and Maf basic leucine zipper protein (MAFK). Till date the above mentioned studies encounter with many limitations [77, 78, 79].
Warfarin is a medication that is commonly used as an anticoagulant which means blood thinner. It aids in treating blood clots such as pulmonary embolism and deep vein thrombosis, and to prevent heart diseases associated with clotting. It has very narrow therapeutic index [80, 81]. However, warfarin therapy may result in complicated adverse reactions including both coagulation and hemorrhage. The racemic mixture of warfarin, S-form is 3–5 times more potent in comparison to R-form of an anticoagulant, and easily gets metabolized due to genetically polymorphic CYP450 isoform i.e. CYP2C9 . CYP2C9 exists in two common polymorphic form, Arg144 > Cys (CYP2C9*2) as well as Ile358 > Leu (CYP2C9*3) modifications in coded sequence of amino acid, with nearly 12 and 5%, respectively. These forms vary between 8 and 10% in Caucasians, with minor occurrence in subject from Southeast Asia. A report in 1999, which confirms patients with one or two common CYP2C9 variant alleles, requires a “low” warfarin dose. These subjects had a risen risk of hemorrhage during warfarin therapy. In 2004, the gene encoded targeting VKORC1 (vitamin K epoxide reductase complex 1) was cloned. In a study, it was found that patients with VKORC1 type of haplotypes requires low dose, the average warfarin maintenance dose was nearly half for subjects with haplotypes having high dose maintenance. In this study, the grouping of both VKORC1 haplotyping as well as genotyping for CYP2C9 described around 25% of dose variance in warfarin. Other studies reported similar results in 2005. The Pharmacogenetics Knowledge Base (PharmGKB), in which data base is reinforced by the National Institutes of Health (NIH) along with part of the NIH Pharmacogenetics Research Network (PGRN), originated an association for consolidation of warfarin pharmacogenetic data throughout the world [83, 84]. In this evaluation of variation in genetic drug target as well as drug metabolism if and only if when VKORC1 and CYP2C9 haplotypes were determined. The figure demonstrates a schematic illustration of both pharmacokinetic (CYP2C9-dependent) as well as pharmacodynamic (VKORC1-dependent) pharmacogenomic aspects that effects final dose of warfarin (Figure 2).
It is important to know that by identifying the individual genetic properties, we can improve the dosing of warfarin. In general, VKORC1 haplotypes have three-fold greater effect on an individual’s warfarin dose than CYP2C9. Both can play a vital role in the potential for estimating the therapeutic warfarin dose. In August 2007, FDA approved a change in labeling of warfarin package stating, “lower starting doses should be considered for patients with some genetic alterations in VKORC1 and CYP2C9 enzymes” .
These cases prove that patient care could be improved effectively by pharmacogenetic based approaches. Although, the allelic occurrences of the gene alterations must be visibly defined in the subjects studied must be well established. Out of above-mentioned cases, no one is absolute, so it is better to perform the sensitivity analyses as well as to regulate the robustness of conclusion with variation in probabilities [85, 86]. Move onward, it is utmost important for maintaining the possible cost-effectiveness of few recently published pharmacogenetic associated reports, for instance, vitamin K epoxide reductase gene variants envisage the warfarin response [87, 88, 89]. Lastly, it will be significant to collect pharmacoeconomic and pharmacogenetic statistics together during industry-funded clinical trials for bringing cost effective theragnostic in a sensible manner [90, 91].
Individualized therapeutics or tailor-made therapy is one of the major goals of pharmacogenomics. In relation to inheritance other factors also contribute to individual therapeutics due to variation in response to administration of drug. Recently many developments in the field of pharmacology and genomics have made possible for physicians to achieve individualization of therapeutics. These recent developments create possibility of thorough basis of particular drug for particular patient with motive of tailor-made therapy. Futuristic development in field of pharmacogenomics has paved the way to new emerging fields of pharmacoproteomic, pharmacotranscriptomics, and pharmacometabolomic. These new branches of science make it possible to achieve the concept of treat each patient as unique, complex, fascinating individual. At the end doubts about achieving individualized therapeutics with the help of this integrated system is still a dream in 21st century era.
The NIPER-R communication number for this manuscript is NIPER-R/Communication/156.
Conflict of interest
The authors declare no conflict of interest among themselves.
Daly AK. Genome-wide association studies in pharmacogenomics. Nature Reviews. Genetics. 2010; 11(4):241-246
Caldwell MD, Berg RL, Zhang KQ , Glurich I, Schmelzer JR, Yale SH, et al. Evaluation of genetic factors for warfarin dose prediction. Clinical Medicine & Research. 2007; 5(1):8-16
Wattanachai N, Kaewmoongkun S, Pussadhamma B, Makarawate P, Wongvipaporn C, Kiatchoosakun S, et al. The impact of non-genetic and genetic factors on a stable warfarin dose in Thai patients. European Journal of Clinical Pharmacology. 2017; 73(8):973-980
Cha PC, Zembutsu H, Takahashi A, Kubo M, Kamatani N, Nakamura Y. A genome-wide association study identifies SNP in DCC is associated with gallbladder cancer in the Japanese population. Journal of Human Genetics. 2012; 57(4):235-237
Chadwick R, Ten Have H, Meslin EM. The Sage Handbook of Health Care Ethics. Sage Publications; 2011
Alsanosi SM, Skiffington C, Padmanabhan S. Pharmacokinetic pharmacogenomics. In: Handbook of Pharmacogenomics and Stratified Medicine. 2014. pp. 341-364
Patil J. Pharmacogenetics and pharmacogenomics: A brief introduction. Journal of Pharmacovigilance. 2015; 2:3-4
Ma Q , Lu AY. Pharmacogenetics, pharmacogenomics, and individualized medicine. Pharmacological Reviews. 2011; 63(2):437-459
Xie HG, Frueh FW. Pharmacogenomics steps toward personalized medicine. Future Medicine. 2005:325-337
Crews KR, Hicks JK, Pui CH, Relling MV, Evans WE. Pharmacogenomics and individualized medicine: Translating science into practice. Clinical Pharmacology and Therapeutics. 2012; 92(4):467-475
Pirmohamed M. Pharmacogenetics and pharmacogenomics. British Journal of Clinical Pharmacology. 2001; 52(4):345
Tambunan US, Zahroh H, Utomo BB, Parikesit AA. Screening of commercial cyclic peptide as inhibitor NS5 methyltransferase of dengue virus through molecular docking and molecular dynamics simulation. Bioinformation. 2014; 10(1):23
Tambunan US, Parikesit AA, Prasetia T, Kerami D. In silico molecular interaction studies of suberoylanilide hydroxamic acid and its modified compounds with histones deacetylase class II Homo sapiensas curative measure towards cervical cancer. Engineering. 2013; 5(10):203-206
Parikesit AA. Introductory chapter: The contribution of bioinformatics as blueprint lead for drug design. Molecular Insight of Drug Design. 2018; 29:7
Handa M, Sharma A, Verma RK, Shukla R. Polycaprolactone based nano-carrier for co-administration of moxifloxacin and rutin and its in-vitro evaluation for sepsis. Journal of Drug Delivery Science and Technology. 2019; 54:101286
Weinshilboum R, Wang L. Pharmacogenomics: Bench to bedside. Nature Reviews. Drug Discovery. 2004; 3(9):739-748
Roses AD. Pharmacogenetics and the practice of medicine. Nature. 2000; 405(6788):857-865
Vizirianakis IS. Challenges in current drug delivery from the potential application of pharmacogenomics and personalized medicine in clinical practice. Current Drug Delivery. 2004; 1(1):73-80
Kalow W. Pharmacogenetics and pharmacogenomics: Origin, status, and the hope for personalized medicine. The Pharmacogenomics Journal. 2006; 6(3):162-165
Hicks JK, McLeod HL. Pharmacogenetics and pharmacogenomics. In: Genomic and Precision Medicine. Academic Press; 2017. pp. 89-107
Shastry BS. Pharmacogenetics and the concept of individualized medicine. The Pharmacogenomics Journal. 2006Jan; 6(1):16-21
Goldstein DB, Tate SK, Sisodiya SM. Pharmacogenetics goes genomic. Nature Reviews. Genetics. 2003; 4(12):937-947
Valeska MD, Adisurja GP, Bernard S, Wijaya RM, Hafidzhah MA, Parikesit AA. The role of bioinformatics in personalized medicine: Your future medical treatment. Cermin Dunia Kedokteran. 2019; 46(12):785-788
Agustriawan DA, Sumarpo AN, Parikesit AA, Nurdiansyah RI, Adisurja GP, Putra AR. In silico study of miRNA-regulated IQ motif-containing GTpase-activating protein family in liver cancer. Asian Journal of Pharmaceutical and Clinical Research. 2018; 11:98-101
National Human Genome Research Institute. All About the Human Genome Project (HGP); 2015
Ikediobi ON, Shin J, Nussbaum RL, Phillips KA, UCSF Center for Translational and Policy Research on Personalized Medicine, Walsh JM, et al. Addressing the challenges of the clinical application of pharmacogenetic testing. Clinical Pharmacology and Therapeutics. 2009; 86(1):28-31
Kim S, Yun YM, Chae HJ, Cho HJ, Ji M, Kim IS, et al. Clinical pharmacogenetic testing and application: Laboratory medicine clinical practice guidelines. Annals of Laboratory Medicine. 2017; 37(2):180-193
Voora D, Ginsburg GS. Clinical application of cardiovascular pharmacogenetics. Journal of the American College of Cardiology. 2012; 60(1):9-20
Ridker PM, Pare G, Parker AN, Zee RY, Miletich JP, Chasman DI. Polymorphism in the CETP gene region, HDL cholesterol, and risk of future myocardial infarction: Genomewide analysis among 18245 initially healthy women from the Women’s Genome Health Study. Circulation. Cardiovascular Genetics. 2009; 2(1):26-33
Anderson CD, Falcone GJ, Phuah CL, Radmanesh F, Brouwers HB, Battey TW, et al. Genetic variants in CETP increase risk of intracerebral hemorrhage. Annals of Neurology. 2016; 80(5):730-740
Maggo SD, Kennedy MA, Clark DW. Clinical implications of pharmacogenetic variation on the effects of statins. Drug Safety. 2011; 34(1):1-9
Xiong Z, Ma A, Chen H. JAK3 inhibitors in organ transplantation and autoimmune disease. Recent Patents on Inflammation & Allergy Drug Discovery. 2010; 4(1):75-81
Säemann MD, Zeyda M, Stulnig TM, Böhmig GA, Wekerle T, Hörl WH, et al. Janus kinase‐3 (JAK3) inhibition: a novel immunosuppressive option for allogeneic transplantation. Transplant International. 2004; 17(9):481-489
Haan C, Rolvering C, Raulf F, Kapp M, Drückes P, Thoma G, et al. Jak1 has a dominant role over Jak3 in signal transduction through γc-containing cytokine receptors. Chemistry & Biology. 2011; 18(3):314-323
Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends in Molecular Medicine. 2001; 7(5):201-204
Shah NJ. Regulation of gene expression. In: Introduction to Basics of Pharmacology and Toxicology. Vol. 1: General and Molecular Pharmacology: Principles of Drug Action. 2019. p. 381
Mini E, Nobili S. Pharmacogenetics: Implementing personalized medicine. Clinical Cases in Mineral and Bone Metabolism. 2009; 6(1):17
Gibbs RA, Belmont JW, Hardenbol P, Willis TD, Yu FL, Yang HM, et al. The international HapMap project. Nature. 2003; 426(6968):789-796
Manolio TA, Collins FS. The HapMap and genome-wide association studies in diagnosis and therapy. Annual Review of Medicine. 2009; 60:443-456
Webb A, Hancock JM, Holmes CC. Phylogenetic inference under recombination using Bayesian stochastic topology selection. Bioinformatics. 2009; 25(2):197-203
Peacock E, Whiteley P. Perlegen sciences, inc. Pharmacogenomics. 2005; 6(4):439-442
Sachidanandam R, Weissman D, Schmidt SC, Kakol JM, Stein LD, Marth G, et al. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature. 2001; 409(6822):928-934
Slate J, Gratten J, Beraldi D, Stapley J, Hale M, Pemberton JM. Gene mapping in the wild with SNPs: guidelines and future directions. Genetica. 2009; 136(1):97-107
Brandl EJ, Kennedy JL, Müller DJ. Pharmacogenetics of antipsychotics. The Canadian Journal of Psychiatry. 2014; 59(2):76-88
Mallal S, Phillips E, Carosi G, Molina JM, Workman C, Tomažič J, et al. HLA-B* 5701 screening for hypersensitivity to abacavir. The New England Journal of Medicine. 2008; 358(6):568-579
Weisberg E, Manley PW, Cowan-Jacob SW, Hochhaus A, Griffin JD. Second generation inhibitors of BCR-ABL for the treatment of imatinib-resistant chronic myeloid leukaemia. Nature Reviews. Cancer. 2007; 7(5):345-356
Dean L. Azathioprine therapy and TPMT and NUDT15 genotype. In: Pratt VM, McLeod HL, Rubinstein WS, et al., editors. Medical Genetics Summaries. Bethesda (MD): National Center for Biotechnology Information (US); 2012
Katz DA, Bhathena A. Overview of pharmacogenetics. Current Protocols in Human Genetics. 2009; 60(1):9-19
Dervieux T, Bala MV. Overview of the pharmacoeconomics of pharmacogenetics. Future Medicine. 2006; 7(8):1175-1184
Chouchana L, Narjoz C, Beaune P, Loriot MA, Roblin X. The benefits of pharmacogenetics for improving thiopurine therapy in inflammatory bowel disease. Alimentary Pharmacology & Therapeutics. 2012; 35(1):15-36
Lin S, Shi Q , Nix FB, Styblo M, Beck MA, Herbin-Davis KM, et al. A novel S-adenosyl-L-methionine: Arsenic (III) methyltransferase from rat liver cytosol. The Journal of Biological Chemistry. 2002; 277(13):10795-10803
Roje S. S-Adenosyl-L-methionine: Beyond the universal methyl group donor. Phytochemistry. 2006; 67(15):1686-1698
Chouchana L, Narjoz C, Roche D, Golmard JL, Pineau B, Chatellier G, et al. Interindividual variability in TPMT enzyme activity: 10 years of experience with thiopurine pharmacogenetics and therapeutic drug monitoring. Pharmacogenomics. 2014; 15(6):745-757
Lennard L. Implementation of TPMT testing. British Journal of Clinical Pharmacology. 2014; 77(4):704-714
Ford LT, Berg JD. Thiopurine S-methyltransferase (TPMT) assessment prior to starting thiopurine drug treatment; a pharmacogenomic test whose time has come. Journal of Clinical Pathology. 2010; 63(4):288-295
Moran GW, Dubeau MF, Kaplan GG, Yang H, Eksteen B, Ghosh S, et al. Clinical predictors of thiopurine-related adverse events in Crohn’s disease. World Journal of Gastroenterology: WJG. 2015; 21(25):7795
Roberts RL, Barclay ML. Current relevance of pharmacogenetics in immunomodulation treatment for Crohn’s disease. Journal of Gastroenterology and Hepatology. 2012; 27(10):1546-1554
Wang L, Weinshilboum R. Thiopurine S-methyltransferase pharmacogenetics: insights, challenges and future directions. Oncogene. 2006; 25(11):1629-1638
Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, et al. A landscape of pharmacogenomic interactions in cancer. Cell. 2016; 166(3):740-754
Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, et al. The cancer genome atlas pan-cancer analysis project. Nature Genetics. 2013; 45(10):1113
Geeleher P, Zhang Z, Wang F, Gruener RF, Nath A, Morrison G, et al. Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies. Genome Research. 2017; 27(10):1743-1751
Hughes CA, Foisy MM, Dewhurst N, Higgins N, Robinson L, Kelly DV, et al. Abacavir hypersensitivity reaction: An update. The Annals of Pharmacotherapy. 2008; 42(3):387-396
Phillips EJ, Chung WH, Mockenhaupt M, Roujeau JC, Mallal SA. Drug hypersensitivity: Pharmacogenetics and clinical syndromes. The Journal of Allergy and Clinical Immunology. 2011; 127(3):S60-S66
Faruki H, Lai-Goldman M. HLA-B* 5701 screening for hypersensitivity to abacavir. Future Medicine. 2008; 5(3):297-300
Mallal S, Nolan D, Witt C, Masel G, Martin AM, Moore C, et al. Association between presence of HLA-B* 5701, HLA-DR7, and HLA-DQ3 and hypersensitivity to HIV-1 reverse-transcriptase inhibitor abacavir. The Lancet. 2002; 359(9308):727-732
Mega JL, Stitziel NO, Smith JG, Chasman DI, Caulfield MJ, Devlin JJ, et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. The Lancet. 2015; 385(9984):2264-2271
Athyros VG, Tziomalos K, Gossios TD, Griva T, Anagnostis P, Kargiotis K, et al. Safety and efficacy of long-term statin treatment for cardiovascular events in patients with coronary heart disease and abnormal liver tests in the Greek Atorvastatin and Coronary Heart Disease Evaluation (GREACE) Study: A post-hoc analysis. The Lancet. 2010; 376(9756):1916-1922
PEACE Trial Investigators. Angiotensin-converting–enzyme inhibition in stable coronary artery disease. The New England Journal of Medicine. 2004; 351(20):2058-2068
Santos PC, Krieger JE, Pereira AC. Renin–angiotensin system, hypertension, and chronic kidney disease: Pharmacogenetic implications. Journal of Pharmacological Sciences. 2012; 120(2):77-88
Trost JC, Lange RA. Treatment of acute coronary syndrome: Part 2: ST-segment elevation myocardial infarction. Critical Care Medicine. 2012; 40(6):1939-1945
Goedde HW, HW G. Pharmacogenetics of cholinesterase: New variants and suxamethonium sensitivity. Cell Therapy and Gene Therapy. 1979; 25(9):219-224
Rosenberg H, Pollock N, Schiemann A, Bulger T, Stowell K. Malignant hyperthermia: A review. Orphanet Journal of Rare Diseases. 2015; 10(1):93
Gätke MR, Bundgaard JR, Viby-Mogensen J. Two novel mutations in the BCHE gene in patients with prolonged duration of action of mivacurium or succinylcholine during anaesthesia. Pharmacogenetics and Genomics. 2007; 17(11):995-999
Wichmann S, Færk G, Bundgaard JR, Gätke MR. Patients with prolonged effect of succinylcholine or mivacurium had novel mutations in the butyrylcholinesterase gene. Pharmacogenetics and Genomics. 2016; 26(7):351-356
Daly AK. Pharmacogenetics and human genetic polymorphisms. The Biochemical Journal. 2010; 429(3):435-449
Zager EM, McNerney R. Multidrug-resistant tuberculosis. BMC Infectious Diseases. 2008; 8(1):1-5
Warren RM, Streicher EM, van Pittius NG, Marais BJ, Van der Spuy GD, Victor TC, et al. The clinical relevance of mycobacterial pharmacogenetics. Tuberculosis. 2009; 89(3):199-202
Motta I, Calcagno A, Bonora S. Pharmacokinetics and pharmacogenetics of anti-tubercular drugs: A tool for treatment optimization? Expert Opinion on Drug Metabolism & Toxicology. 2018; 14(1):59-82
Sim E, Laurieri N. Isoniazid induced toxicity: Systemic lupus erythematosus. Journal of Drug Design and Research. 2018; 49:51
Rieder MJ, Reiner AP, Gage BF, Nickerson DA, Eby CS, McLeod HL, et al. Effect of VKORC1 haplotypes on transcriptional regulation and warfarin dose. The New England Journal of Medicine. 2005; 352(22):2285-2293
Johnson JA, Cavallari LH. Warfarin pharmacogenetics. Trends in Cardiovascular Medicine. 2015; 25(1):33-41
Gulseth MP, Grice GR, Dager WE. Pharmacogenomics of warfarin: Uncovering a piece of the warfarin mystery. American Journal of Health-System Pharmacy. 2009; 66(2):123-133
Kamali F, Wynne H. Pharmacogenetics of warfarin. Annual Review of Medicine. 2010; 61:63-75
Limdi NA, Veenstra DL. Warfarin pharmacogenetics. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy. 2008; 28(9):1084-1097
Chasman DI, Ridker PM. Pharmacogenetics: The outlook for genetic testing in statin therapy. Nature Clinical Practice. Cardiovascular Medicine. 2005; 2(1):2-3
Haase CG, Zuhlsdorf M, Kuhlmann J. The implication of pharmacogenomics/pharmacogenetics on the treatment of neurological diseases. Aktuelle Neurologie. 2002; 29(7):333-337
Wadelius M, Pirmohamed M. Pharmacogenetics of warfarin: Current status and future challenges. The Pharmacogenomics Journal. 2007; 7(2):99-111
Li T, Chang CY, Jin DY, Lin PJ, Khvorova A, Stafford DW. Identification of the gene for vitamin K epoxide reductase. Nature. 2004; 427(6974):541-544
Lurie Y, Loebstein R, Kurnik D, Almog S, Halkin H. Warfarin and vitamin K intake in the era of pharmacogenetics. British Journal of Clinical Pharmacology. 2010; 70(2):164-170
Dervieux T, Meshkin B, Neri B. Pharmacogenetic testing: Proofs of principle and pharmacoeconomic implications. Mutation Research, Fundamental and Molecular Mechanisms of Mutagenesis. 2005; 573(1-2):180-194
Vegter S, Boersma C, Rozenbaum M, Wilffert B, Navis G, Postma MJ. Pharmacoeconomic evaluations of pharmacogenetic and genomic screening programmes. PharmacoEconomics. 2008; 26(7):569-587